Computational imaging and intelligent specificity (Anastasio)

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SUMMARY In this technology research and development (TRD) project, advanced computational and machine learning methods will be developed that address a variety of needs related to image formation and image analysis in high-resolution label-free optical microscopy. Computational methods are being rapidly deployed that are changing the way that measurement data are acquired and improving the formation and analysis of microscopy images. The potential impact of such methods on the field of label-free microscopy is very high and can optimally leverage inherent endogenous contrast mechanisms in innovative and informative ways. The developed methods will serve as enabling technologies for many projects in the proposed center. The research will be informed by and jointly developed and evaluated with the TRD and driving biological projects. A general theme of this work is the integration of imaging science, physics- and deep learning (DL)-based approaches to circumvent the limitations of label-free imaging and the use of objective image quality measures to systematically validate and refine the developed methods. Three broad classes of computational methods will be investigated that will enable the (1) image-to-image mapping of label-free images to provide computational specificity, improved semantic segmentation, and/or enhanced spatial resolution; (2) improved reconstruction of images for 3D cellular imaging; and (3) extraction of biologically relevant information from multi-modality label-free image data. The Specific Aims of the project are: Aim 1: Image-to-image translation methods for providing specificity, semantic segmentation, and/or enhanced spatial resolution; Aim 2: Diffraction tomography and inverse scattering methods for 3D imaging; and Aim 3: Biomarker discovery and multi-modal DL methods. This successful completion of this project will result in computational and DL methods that will advance a variety of label-free imaging technologies. These methods will enable improved computational staining, enhance of spatial resolution, semantic segmentation, 3D image formation, and analysis of multi-modality label-free image data. They will be systematically validated for use in the biomedical applications that are within the purview of the proposed P41 center. All source code, trained models and documentation will be made open-source and shared online.